where \(\lambda \)  and \(\beta \)  are parameters learned from the target data, and \(\mu (x)\)  and \(\sigma (x)\)  are the mean and standard deviation computed independently for each feature channel. Instead, Xiao et al. [1]} proposed a domain-guided dropout to solve the problem of multi-da muting neurons that are not effective and related for each domain. Instead of exploiting a specific dropout rate, they assigned dropout depending on the gain of the loss function of each neuron on the domain sample when the specific neuron is removed.
